A New Classification Method for Breast Cancer Diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)

• Published in: Advances in Natural Computation pp. 830 - 838
• Main Authors: Polat, Kemal, Sahan, Seral, Kodaz, Halife, Günes, Salih
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial Immune System; Artificial intelligence; Breast Cancer Diagnosis; Computer science; control theory; systems; Decision Tree Algorithm; Exact sciences and technology; Feature Selection; High Classification Accuracy; Human; Validation; Cancerology; Classification; Diagnosis; Mammary gland; Pattern recognition; Malignant tumor; Decision tree; Immune system
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_117

In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with a new approach, FS-AIRS (Feature Selection Artificial Immune Recognition System) algorithm that has an important place in classification systems and was developed depending on the Artificial Immune Systems. With this purpose, 683 data in the Wisconsin breast cancer dataset (WBCD) was used. In this study, differently from the studies in the literature related to this concept, firstly, the feature number of each data was reduced to 6 from 9 in the feature selection sub-program by means of forming rules related to the breast cancer data with the C4.5 decision tree algorithm. After separating the 683 data set with reduced feature number into training and test sets by 10 fold cross validation method in the second stage, the data set was classified in the third stage with AIRS and a quite satisfying result was obtained with respect to the classification accuracy compared to the other methods used for this classification problem.